Method and system for controlling swarming communication of uav group based on link lifetime perception

Abstract

The application provides a link lifetime-aware UAV swarm swarming communication control method and system, comprising the following steps: S1. constructing a UAV self-organizing network; setting a state field for each UAV node; setting a task field for each task; calculating the lifetime-weighted link cost between any two UAV nodes based on the UAV self-organizing network; adopting a multi-task preemption scheduling strategy, sorting the tasks according to the task priority and generation time, using the lifetime-weighted Dijkstra algorithm, and assigning a path to the task based on the lifetime-weighted link cost between any two nodes, locking the UAV node and updating the state field and the task field of the UAV node, dynamically adjusting the priority of the task which is not successfully assigned based on a dynamic adjustment rule; and releasing the node and triggering the next round of scheduling after the task is completed. The application significantly improves the on-time delivery rate of critical tasks, reduces the risk of link retransmission and congestion, and guarantees the service fairness of low-priority tasks.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) communication technology, and particularly relates to a method and system for controlling UAV swarm communication based on link lifetime awareness. Background Technology

[0002] In recent years, drones have been widely used in environmental monitoring, disaster relief, smart agriculture, and integrated air-space-ground communications. Compared with traditional terrestrial wireless networks, self-organizing networks composed of multiple drones have advantages such as rapid deployment and abundant line-of-sight links. However, they also face inherent constraints such as highly dynamic topology, susceptibility to link interruptions, and limited node computing power and energy. In real-world missions, there is often a need for multiple drones to simultaneously upload large amounts of data (such as high-definition video or batch sensor information) to a central base station / server. However, reliable single-hop direct links are not always available, inevitably requiring reliance on other drone nodes for multi-hop relay.

[0003] In typical applications, a drone network may simultaneously handle several heterogeneous tasks: emergency image transmission, routine inspection data, and low-priority log uploads. A node's wireless transceiver port, buffer, and energy can only be occupied by one data stream at a time. When multiple tasks compete concurrently, without fine-grained scheduling, high-priority tasks can easily be blocked or low-priority tasks can be starved for extended periods, leading to network congestion and global performance collapse.

[0004] To address the issue of unstable drone links, most existing research attempts still remain at the perspective of single task and single route, lacking a systematic consideration of global resource scheduling for multiple tasks. In addition, mainstream methods usually assume the existence of a centralized controller, which is difficult to adapt to the distributed constraints in drone swarms where "each drone needs to make independent decisions and can only obtain information from its neighbors".

[0005] Swarm control, inspired by the group behavior in nature, emphasizes global coordination through local rules. Introducing it into communication networks allows for the adjustment of node transmission rates and forwarding directions without central coordination, naturally aligning with the distributed, dynamic, and adaptive characteristics of drone swarms. However, existing swarm communication control primarily focuses on traffic balancing and congestion avoidance, failing to incorporate link lifetime and multi-task prioritization into a unified framework.

[0006] In summary, for multi-UAV self-organizing networks, a system is needed that can meet the following requirements:

[0007] Real-time assessment of remaining link lifespan, considering both latency and failure risk during the pathfinding process, to avoid mid-way disconnection caused by "short-lived links".

[0008] It supports multi-task priority, preemption and aging mechanisms to ensure that critical tasks are completed in a timely manner and that low-priority tasks can eventually be served.

[0009] It operates in a fully distributed manner, relying solely on neighbor broadcasts, to adapt to the reality of drones constantly moving and temporarily joining / leaving the network;

[0010] Control network congestion and node conflicts to prevent multiple tasks from occupying the same relay node simultaneously. Summary of the Invention

[0011] To address the issues of insufficient remaining link lifetime and congestion and task starvation caused by concurrent tasks competing for node resources in high-speed dynamic topology drone swarms, this invention proposes a drone swarm swarm congestion communication control method and system based on link lifetime awareness.

[0012] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0013] A method for controlling drone swarm swarm communication based on link lifetime awareness includes the following steps:

[0014] Step S1. Construct a self-organizing network of drones, in which multiple data acquisition drones perform multiple data acquisition tasks according to a preset trajectory and transmit the data to the central base station / server through multi-hop relay;

[0015] Step S2. Set status fields for each drone node, including: node occupancy flag, current occupancy task number, and remaining node lock duration;

[0016] Step S3. Set task fields for each task, including: unique identifier, source node, destination node, data volume, dynamic priority, and task status;

[0017] Step S4. Based on the UAV self-organizing network, calculate the lifetime-weighted link cost between any two UAV nodes;

[0018] Step S5. A multi-task preemptive scheduling strategy is adopted, tasks are sorted according to task priority and generation time, lifespan-weighted Dijkstra algorithm is used, and a path is allocated for the task based on the lifespan-weighted link cost between any two nodes, the UAV node is locked and the status field and task field of the UAV node are updated, and the priority of unsuccessfully allocated tasks is dynamically adjusted based on dynamic adjustment rules.

[0019] Step S6. After the task is completed, release the node and trigger the next round of scheduling.

[0020] Furthermore, the drone self-organizing network in step S1 includes:

[0021] Multiple data acquisition drones execute multiple data acquisition tasks according to preset trajectories, and transmit data to a designated central base station / server via wireless links in a multi-hop manner; each task is defined as the amount of data transmitted from the source node to the destination node. Data packets that are transmitted when the distance between the source node and the destination node exceeds the maximum reliable transmission distance threshold. At this time, it is necessary to use other drone nodes in the network for multi-hop relay forwarding.

[0022] Furthermore, the lifetime-weighted link cost between any two UAV nodes in step S4 is:

[0023]

[0024] in, Indicates at time Neighbor nodes The lifetime-weighted link cost between them Indicates at time Neighbor nodes Stable data packet transmission The required latency; Risk aversion coefficient; Indicates at time node The remaining lifetime of the link between them; This is a penalty item for link failure risk, reflecting the time required for transmission. with spare time The ratio; when this ratio increases, it means that the risk of the remaining link lifetime being insufficient to support the completion of the task increases, thus significantly increasing the link cost.

[0025] Furthermore, at any time drone nodes and nodes Remaining lifetime of the link for:

[0026]

[0027] in, This indicates retrieving the maximum value from the set. Indicates from time Beginning, after The distance between the two nodes after one second; This represents the maximum distance threshold at which drones can maintain reliable communication. This indicates the duration for which the link can remain effective; therefore... The physical meaning is that, from the current moment... From the beginning, until some point in the future The longest period of time a link can remain continuously effective until it is first terminated. .

[0028] Furthermore, the mechanism for locking the drone node in step S5 includes:

[0029] When a node is occupied by a task, its occupation mark is set to occupied status, the task number is written, the lock countdown is set to the estimated transmission time of the task, and the status is broadcast to neighboring nodes.

[0030] After the lock countdown reaches zero, the node is automatically reset to an idle state and the task number is cleared, and the status update is broadcast again; a new task is allowed to occupy the node only when it is in an idle state.

[0031] Furthermore, the multi-task preemptive scheduling strategy includes:

[0032] Filter all tasks in the waiting state, sort them from highest to lowest priority value, and sort tasks with the same priority from earliest to latest creation time; sequentially search for the source-to-destination path with the lowest lifetime-weighted link cost in the set of idle nodes for each task; if the path is found successfully, mark the path node as occupied and associate it with the current task, and update the task to running status; for tasks that are not successfully assigned, increase their priority value by one level.

[0033] Furthermore, the dynamic adjustment rule for priority is as follows:

[0034] The initial priority of a task is determined by its urgency.

[0035] The priority value is increased by one level after each scheduling failure.

[0036] High-priority tasks can preempt node resources already occupied by low-priority tasks.

[0037] Furthermore, the lifetime-weighted Dijkstra algorithm performs the following operations:

[0038] Construct a network topology graph based on the set of idle nodes;

[0039] Assign a lifetime-weighted link cost value to each edge in the network topology graph;

[0040] The shortest path algorithm is used to iteratively calculate the shortest path from the source node to the destination node with the lifetime-weighted link cost.

[0041] A complete transmission path is generated by tracing back the predecessor node.

[0042] Furthermore, it also includes a link failure handling mechanism:

[0043] If insufficient remaining link lifetime is detected during data transmission, the task will be immediately terminated and a node release command will be broadcast.

[0044] The status of the interrupted task will be reset to the waiting state and added back to the scheduling queue.

[0045] On the other hand, the present invention provides a swarm communication control system for unmanned aerial vehicles (UAVs) based on link lifetime awareness, comprising:

[0046] Self-organizing network building module. It is used to build a drone self-organizing network in which multiple data collection drones transmit data to a central base station / server through multi-hop relays;

[0047] Status field setting module. It is used to set status fields for each drone node, including: node occupancy flag, current occupancy task number, and remaining node lock duration;

[0048] The task field setting module is used to set task fields for each task, including: unique identifier, source node, destination node, data volume, dynamic priority, and task status.

[0049] Link cost calculation module. It is used to calculate the lifetime-weighted link cost between any two UAV nodes based on the UAV self-organizing network;

[0050] The path calculation module is used to employ a multi-task preemptive scheduling strategy, sort tasks by task priority and generation time, use the lifetime-weighted Dijkstra algorithm, and allocate paths for tasks based on the lifetime-weighted link cost between any two nodes, lock UAV nodes, and update the status field and task field of the UAV nodes. Tasks that are not successfully allocated will have their priorities dynamically adjusted based on dynamic adjustment rules.

[0051] The scheduling module is used to release nodes and trigger the next round of scheduling after a task is completed.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] In a fully distributed UAV self-organizing network, this invention realizes link lifetime-aware path selection, task priority scheduling, and node resource mutual exclusion control, significantly improving the timely delivery rate of critical tasks, reducing the risk of link retransmission and congestion, and ensuring the service fairness of low-priority tasks. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 This is the overall flowchart of the present invention.

[0056] Figure 2 This invention relates to a multi-UAV self-organizing network.

[0057] Figure 3 This is a flowchart of the multi-task preemptive path allocation strategy of the present invention.

[0058] Figure 4 This is a schematic diagram of the start and end times and path selection for the transmission of 35 tasks in an embodiment of the present invention.

[0059] Figure 5 This is a Gantt chart showing the execution of 35 tasks in an embodiment of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0061] Example 1

[0062] The invention will now be further described with reference to the accompanying drawings.

[0063] In this embodiment, 10 drones are randomly distributed within a 500m × 500m airspace. Each drone employs a random movement model: flying at a random speed of 10-15m / s in a random direction, pausing for 2 seconds after reaching a new location. Simultaneously, each drone's new location must be within the communication radius (250m) of at least one other drone. The continuous movement of the drones causes the network topology to change dynamically every second, and the link duration (lifetime) is determined by the relative motion between the drones.

[0064] The following is combined Figure 1 This invention introduces a link lifetime-aware distributed swarm communication control method for UAV swarms, comprising the following steps:

[0065] Step S1. Construct a self-organizing network of drones, in which multiple data collection drones transmit data to the central base station / server through multi-hop relays;

[0066] As shown in Figure 2, Each data-collecting drone executes a data collection task according to a preset trajectory and transmits the data to a designated central base station / server via a multi-hop wireless link. Each task is defined as the amount of data transmitted from the source node to the destination node. Data packets. When the distance between the source node and the destination node exceeds the maximum reliable transmission distance threshold. In such cases, multi-hop relay forwarding is required using other drone nodes in the network to ensure that the data successfully reaches the destination node.

[0067] Step S2. Set status fields for each drone node, including: node occupancy flag, current occupancy task number, and remaining node lock duration;

[0068] The status fields in this embodiment are shown in the following table:

[0069]

[0070] Step S3. Set task fields for each task, including: unique identifier, source node, destination node, data volume, dynamic priority, and task status;

[0071] The task fields in this embodiment are shown in the following table:

[0072]

[0073] Step S4. Based on the UAV self-organizing network, calculate the lifetime-weighted link cost between any two UAV nodes;

[0074] Each drone clearly knows the movement trajectories of all other drones and the specific coordinates of the central base station / server. Therefore, at any given moment... Any two nodes and The distance can be determined by Calculation. Among them, Representing nodes respectively and At any moment The location coordinates. The transmission rate can be determined by... Calculation, where It's bandwidth. It is transmission power.

[0075] It is the channel gain between the two drones. It is a distance of 1 m Channel gain at that location, This is the path loss coefficient. Then in Stable data packet transmission The delay can be caused by calculate.

[0076] Only the distance between nodes Only when the distance exceeds this threshold can a stable and effective communication link exist between them. When the distance exceeds this threshold, the link is considered to be interrupted. Due to the mobility of UAV nodes, the remaining link lifetime (LLT) and transmission rate between any two nodes change dynamically over time. drone nodes and nodes Remaining lifetime of the link The calculation is as follows:

[0077]

[0078] The above formula means finding a maximum value. ,exist Within this time frame, at every moment Below, drone nodes and nodes The distances are all less than the threshold. .this It refers to the remaining lifetime of the downlink at time t. Because drone network topology is highly dynamic and links can be disconnected at any time, it is necessary to consider not only transmission latency but also whether the link duration is sufficient to support data transmission.

[0079] To address this, this application proposes a novel path selection metric—lifetime-weighted link cost. This metric comprehensively considers the impact of transmission latency and link duration on path availability, more accurately reflecting the actual transmission status of links in dynamic UAV network environments, and facilitating the effective selection of more stable and reliable transmission paths. At any given time... Neighbor nodes The lifetime-weighted link cost between them can be determined by .in, This is the risk aversion coefficient (the larger the coefficient, the more sensitive the user is to link failure). The link has failed. According to the formula... This allows us to obtain the total distance along the entire link from the source node to the target node. Subsequently, we will use Dijkstra's algorithm to find a path with the minimum total lifetime weighted link cost for each source node.

[0080] Step S5. A multi-task preemptive scheduling strategy is adopted, tasks are sorted according to task priority and generation time, lifespan-weighted Dijkstra algorithm is used, and a path is allocated for the task based on the lifespan-weighted link cost between any two nodes, the UAV node is locked and the status field and task field of the UAV node are updated, and the priority of unsuccessfully allocated tasks is dynamically adjusted based on dynamic adjustment rules.

[0081] When multiple tasks request transmission simultaneously, node contention may occur, meaning a drone node can only serve one task at a time. To avoid this conflict, this invention introduces a status flag for each node: busy_flag, task_id, and TTL. Whenever a node is preempted, it broadcasts its own status information, allowing all drones in the network to understand the current resource usage in real time. After TTL seconds, the node will automatically release itself, the task_id will be cleared, the busy_flag will be reset to zero, and the node will broadcast its status information again.

[0082] Because different types of tasks have varying sensitivities to transmission latency, this embodiment introduces a priority mechanism in task scheduling to effectively meet task timeliness requirements and avoid individual tasks being unable to obtain node resources for extended periods (i.e., task "starvation"). The specific rules are as follows:

[0083] Each task has an initial priority due to its different type;

[0084] During the scheduling process, all tasks are preempted in the order of priority plus the timestamp generated.

[0085] If a task fails to occupy a node in a scheduling round, its priority will be automatically increased by one in the next scheduling round to ensure that low-priority tasks also have a chance to complete and avoid starvation.

[0086] like Figure 3 As shown, the multi-task preemptive path allocation strategy process in this embodiment is as follows:

[0087] The scheduling loop continues to fire while the queue is not empty:

[0088] Task sorting: Extract all tasks with status=WAIT, sort them by "high priority → low priority, then by creation time → late priority";

[0089] Node preemption and path optimization: For each sorted task, the lifetime-weighted Dijkstra algorithm searches the current set of idle nodes for the shortest lifetime-weighted link cost from the source node to the target node; if a feasible path is found, the involved nodes are locked (by setting busy_flag=1, writing task_id, and setting...). (Estimated transmission time), and set the task status to RUN; otherwise, the task remains in the queue waiting;

[0090] Priority aging: Tasks that are not successfully assigned in this round will have their priority increased by ++, ensuring that low-priority tasks will eventually get a service opportunity.

[0091] The specific process of lifetime-weighted Dijkstra's algorithm is as follows:

[0092] Input: Set of free nodes V, set of available edges E, LLT sum of each edge Source node s, destination node d;

[0093] Preprocessing: Calculate the weight w(u,v) for each element in E;

[0094] Core iteration: Maintain a min-heap Q to store the cumulative cost dist[] of undetermined nodes, and cyclically pop the smallest dist node and relax its adjacent edges;

[0095] Termination and backtracking: The process ends when the popped node is d or the heap is empty; if dist[d]=∞, the current topology is considered unreachable, otherwise backtracking is performed according to prev[] to obtain the optimal path and return it to the scheduler.

[0096] When a task is in the RUN state, the source node continuously sends data to the central UAV along the locked path; once the task is completed, all nodes on the path are released (the triplet is reset and broadcast), the task status is set to DONE, it is removed from the queue, and the next round of scheduling is triggered.

[0097] In this embodiment, the Dijkstra algorithm's path finding process is as follows:

[0098] Input information:

[0099] Current available node set (Only nodes with busy_flag=0)

[0100] Current available edge set :satisfy And both endpoints are in .

[0101] Instantaneous rate of each available edge Remaining lifespan .

[0102] Source node Target node .

[0103] Output:

[0104] from arrive shortest path Minimum lifetime weighted link cost If from arrive If it is unattainable .

[0105] The specific steps include:

[0106] I. Preprocessing

[0107] For each available communication link (u, v):

[0108] First, calculate the time required for a data packet to be transmitted once through this link. .

[0109] Determine link validity: If the link has a remaining lifetime... Less than or equal to this single-hop transmission time This means that the link may fail before the data packet is fully transmitted. In this case, the link is considered unavailable, and its cost is reduced. Let it be positive infinity. If the remaining lifetime of the link is long enough... Then, the lifetime-weighted distance of the link is calculated according to the algorithm definition. .

[0110] II. Initialization

[0111] Two key variables are maintained for each node: `dist[u]`: records the current known minimum cumulative cost from source node `s` to node `u`. Initially, `dist[s] = 0`, and `dist[u]` for all other nodes is set to positive infinity, indicating that no reachable path has been found or the cost is unknown. `prev[u]`: records the predecessor node of node `u` when the current minimum cumulative cost `dist[u]` is reached. This is used to eventually backtrack the complete path. Initially, `prev[u]` for all nodes is set to empty.

[0112] Create a priority queue (min-heap) Q and add all nodes to the queue. The queue is sorted according to the dist[u] value of each node, and the node with the smallest dist[u] is at the top of the heap and will be processed first.

[0113] III. Finding the shortest path (main loop)

[0114] As long as priority queue Q is not empty, repeat the following process:

[0115] Select the node with the lowest current cost: Take out and remove the node u (the top node of the heap) with the smallest current dist[u] value from queue Q.

[0116] Check if the goal has been achieved: If the extracted node u is the destination node d, it means that the optimal path from s to d has been found, and the loop can be terminated in advance.

[0117] Explore neighboring nodes: If u is not d, then check each of u's neighboring nodes v in turn. If the cost of link (u, v) is... If the value is positive infinity, then ignore this neighbor v and continue checking the next neighbor. For valid links, calculate the cost of a new candidate path: alt = dist[u] + This represents the potential total cost of reaching neighbor v through the current node u. If the calculated candidate cost alt is less than the current record value dist[v] of node v, update dist[v] = alt and prev[v] = u. Call the priority queue DecreaseKey(Q, v) to adjust the position of node v in the priority queue Q to maintain the property of the queue being ordered by dist.

[0118] IV. Tracing the Build Path

[0119] Check the final cumulative cost dist[d] of the destination node d: If dist[d] is still positive infinity, it means that in the current network state, there is no feasible path from the source node s to the destination node d. If dist[d] is a finite value, it means that a feasible path exists. Construct this path by backtracking the predecessor node.

[0120] V. Output the final result

[0121] like Figure 4 and Figure 5 As shown, if the path list `path` is empty, then in the current network state, there is no feasible path from `s` to `d` that satisfies the lifetime constraint. If the path list `path` is not empty, then `Path = [s, …, d]` is the optimal path from the source node `s` to the destination node `d`.

[0122] If flight causes premature link failure, any node on the path that detects insufficient actual LLT will immediately abort and broadcast a release signal. The scheduler will then place the task back into the WAIT queue and quickly reroute. The parameters can flexibly balance latency performance and link stability to adapt to the timeliness or reliability requirements of different scenarios.

[0123] Step S6. After the task is completed, release the node and trigger the next round of scheduling.

[0124] Example 2

[0125] This embodiment provides a swarm communication system for unmanned aerial vehicles (UAVs) based on link lifetime awareness, including:

[0126] Self-organizing network construction module. It is used to build a self-organizing network of drones, in which multiple data acquisition drones perform multiple data acquisition tasks according to a preset trajectory and transmit the data to the central base station / server through multi-hop relay;

[0127] Status field setting module. It is used to set status fields for each drone node, including: node occupancy flag, current occupancy task number, and remaining node lock duration;

[0128] The task field setting module is used to set task fields for each task, including: unique identifier, source node, destination node, data volume, dynamic priority, and task status.

[0129] Link cost calculation module. It is used to calculate the lifetime-weighted link cost between any two UAV nodes based on the UAV self-organizing network;

[0130] The path calculation module is used to employ a multi-task preemptive scheduling strategy, sort tasks by task priority and generation time, use the lifetime-weighted Dijkstra algorithm, and allocate paths for tasks based on the lifetime-weighted link cost between any two nodes, lock UAV nodes, and update the status field and task field of the UAV nodes. Tasks that are not successfully allocated will have their priorities dynamically adjusted based on dynamic adjustment rules.

[0131] The scheduling module is used to release nodes and trigger the next round of scheduling after a task is completed.

[0132] It should be understood that any parts not described in detail in this specification belong to the prior art.

[0133] It should be understood that the above description of the preferred embodiments is quite detailed, but this should not be construed as limiting the scope of protection of this invention. It is neither necessary nor possible to exhaustively describe all possible implementations. Those skilled in the art, guided by this invention, can make substitutions or modifications without departing from the scope of the claims, all of which fall within the scope of protection of this invention. The scope of protection of this invention should be determined by the appended claims.

Claims

1. A method for controlling swarm communication of unmanned aerial vehicles (UAVs) based on link lifetime awareness, characterized in that, Includes the following steps: Step S1. Construct a self-organizing network of drones, in which multiple data acquisition drones perform multiple data acquisition tasks according to a preset trajectory and transmit the data to the central base station / server through multi-hop relay; Step S2. Set status fields for each drone node, including: node occupancy flag, current occupancy task number, and remaining node lock duration; Step S3. Set task fields for each task, including: unique identifier, source node, destination node, data volume, dynamic priority, and task status; Step S4. Based on the UAV self-organizing network, calculate the lifetime-weighted link cost between any two UAV nodes; the lifetime-weighted link cost between any two UAV nodes is: in, Indicates at time Neighbor nodes The lifetime-weighted link cost between them Indicates at time Neighbor nodes Stable data packet transmission The required latency; Risk aversion coefficient; Indicates at time node The remaining lifetime of the link between them; This is a penalty item for link failure risk, reflecting the time required for transmission. with spare time The ratio; when this ratio increases, it means that the risk of the remaining link lifetime being insufficient to support the completion of the task increases, thus significantly increasing the link cost; At any moment drone nodes and nodes Remaining lifetime of the link for: in, This indicates retrieving the maximum value from the set. Indicates from time Beginning, after The distance between the two nodes after one second; This represents the maximum distance threshold at which drones can maintain reliable communication. This indicates the duration for which the link can remain effective; therefore... The physical meaning is that, from the current moment... From the beginning, until some point in the future The longest period of time a link can remain continuously effective until it is first terminated. ; Step S5. A multi-task preemptive scheduling strategy is adopted, tasks are sorted according to task priority and generation time, lifespan-weighted Dijkstra algorithm is used, and a path is allocated for the task based on the lifespan-weighted link cost between any two nodes, the UAV node is locked and the status field and task field of the UAV node are updated, and the priority of unsuccessfully allocated tasks is dynamically adjusted based on dynamic adjustment rules. Step S6. After the task is completed, release the node and trigger the next round of scheduling.

2. The method for controlling UAV swarm swarm communication based on link lifetime awareness according to claim 1, characterized in that, The drone self-organizing network in step S1 includes: Multiple data acquisition drones execute multiple data acquisition tasks according to preset trajectories, and transmit the data to the central base station / server via multi-hop relay. Each task involves transmitting a certain amount of data from the source node to the destination node. Data packets that are transmitted when the distance between the source node and the destination node exceeds the maximum reliable transmission distance threshold. At the same time, it uses other drone nodes in the network for multi-hop relay forwarding.

3. The method for controlling UAV swarm swarm communication based on link lifetime awareness according to claim 1, characterized in that, The mechanism for locking the drone node in step S5 includes: When a node is occupied by a task, its occupation mark is set to occupied status, the task number is written, the lock countdown is set to the estimated transmission time of the task, and the status is broadcast to neighboring nodes. After the lock countdown reaches zero, the node is automatically reset to an idle state and the task number is cleared, and the status update is broadcast again; a new task is allowed to occupy the node only when it is in an idle state.

4. The method for controlling UAV swarm swarm communication based on link lifetime awareness according to claim 3, characterized in that, The multi-task preemptive scheduling strategy includes: Filter all tasks in the waiting state, sort them from highest to lowest priority value, and sort tasks with the same priority from earliest to latest creation time; sequentially search for the source-to-destination path with the lowest lifetime-weighted link cost in the set of idle nodes for each task; if the path is found successfully, mark the path node as occupied and associate it with the current task, and update the task to running status; for tasks that are not successfully assigned, increase their priority value by one level.

5. The method for controlling UAV swarm swarm communication based on link lifetime awareness according to claim 4, characterized in that, The dynamic adjustment rule for priority is as follows: The initial priority of a task is determined by its urgency. The priority value is increased by one level after each scheduling failure. High-priority tasks can preempt node resources already occupied by low-priority tasks.

6. The method for controlling UAV swarm swarm communication based on link lifetime awareness according to claim 5, characterized in that, The lifetime-weighted Dijkstra algorithm performs the following operations: Construct a network topology graph based on the set of idle nodes; Assign a lifetime-weighted link cost value to each edge in the network topology graph; The shortest path algorithm is used to iteratively calculate the shortest path from the source node to the destination node with the lifetime-weighted link cost. A complete transmission path is generated by tracing back the predecessor node.

7. A method for controlling UAV swarm swarm communication based on link lifetime awareness according to claim 6, characterized in that, It also includes a link failure handling mechanism: If insufficient remaining link lifetime is detected during data transmission, the task will be immediately terminated and a node release command will be broadcast. The status of the interrupted task will be reset to the waiting state and added back to the scheduling queue.

8. A swarm communication control system for unmanned aerial vehicles (UAVs) based on link lifetime awareness, characterized in that, include: Self-organizing network construction module. It is used to build a self-organizing network of drones, in which multiple data acquisition drones perform multiple data acquisition tasks according to a preset trajectory and transmit the data to the central base station / server through multi-hop relay; Status field setting module. It is used to set status fields for each drone node, including: node occupancy flag, current occupancy task number, and remaining node lock duration; The task field setting module is used to set task fields for each task, including: unique identifier, source node, destination node, data volume, dynamic priority, and task status. Link cost calculation module. It is used to calculate the lifetime-weighted link cost between any two UAV nodes based on the UAV self-organizing network; The path calculation module is used to employ a multi-task preemptive scheduling strategy, sort tasks by task priority and generation time, use the lifetime-weighted Dijkstra algorithm, and allocate paths for tasks based on the lifetime-weighted link cost between any two nodes, lock UAV nodes, and update the status field and task field of the UAV nodes. Tasks that are not successfully allocated will have their priorities dynamically adjusted based on dynamic adjustment rules. The scheduling module is used to release nodes and trigger the next round of scheduling after a task is completed. The link lifetime-aware UAV swarm swarm communication control system is used to execute the steps in the link lifetime-aware UAV swarm swarm communication control method according to any one of claims 1-7.

Images